Statistical Approach To Wind Power Forecasting
Statistical prediction methods are based on one or several models that establish the relation between historical values of power, as well as historical and forecast values of meteorological variables, and wind power measurements. The physical phenomena are not decomposed and accounted for, even if expertise of the problem is crucial for choosing the right meteorological variables and designing suitable models. Model parameters are estimated from a set of past available data, and they are regularly updated during online operation by accounting for any newly available information (i.e. meteorological forecasts and power measurements).
Statistical models include linear and non-linear models, but also structural and black-box types of models. Structural models rely on the analyst’s expertise on the phenomenon of interest while black-box models require little subject-matter knowledge and are constructed from data in a fairly mechanical way. Concerning wind power forecasting, structural models would be those that include a modeling of the diurnal wind speed variations, or an explicit function of meteorological variable predictions. Black-box models include most of the artificial-intelligence-based models such as Neural-Networks (NNs) and Support Vector Machines (SVMs). However, some models are ‘in-between’ the two extremes of being completely black-box or structural. This is the case of expert systems, which learn from experience (from a dataset), and for which prior knowledge can be injected. We then talk about grey-box modeling. Statistical models are usually composed by an autoregressive part, for seizing the persistent behavior of the wind, and by a ‘meteorological’ part, which consists in the nonlinear transformation of meteorological variable forecasts. The autoregressive part permits to significantly enhance forecast accuracy for horizons up to 6–10 hours ahead, i.e. over a period during which the sole use of meteorological forecast information may not be sufficient for outperforming persistence.
Today, major developments of statistical approaches to wind power prediction concentrate on the use of multiple meteorological forecasts (from different meteorological offices) as input and forecast combination, as well as on the optimal use of spatially distributed measurement data for prediction error correction, or alternatively for issuing warnings on potentially large uncertainty.
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